The present invention relates generally to the field of cardiology and in particular to detection and analysis of cardiac function. There is a serious need for detection of normal and abnormal cardiac rhythms using heart rate (HR) or interbeat interval series.
Several common clinical scenarios call for identification of cardiac rhythm in ambulatory out-patients. For example, atrial fibrillation (AF) is a common arrhythmia that is often paroxysmal in nature. Decisions about its therapy are best informed by knowledge of the frequency, duration and severity of the arrhythmia. While implanted devices can record this information with great accuracy, non-invasive diagnostic devices for recording electrocardiographic (EKG) signals are constrained by the need for skin electrodes. Non-invasive devices for determining heart rate from the pulse rate are not in common use because of reduced confidence in detecting AF based on the heart rate series alone. Specifically, sinus rhythm with frequent ectopy is expected to share many time series features with AF, and thus be difficult to distinguish. In addition, many other transient cardiac arrhythmias cause short-lived symptoms but are currently difficult to diagnose on the basis of heart rate time series alone.
Thus a need exists for confident diagnosis of normal and abnormal cardiac rhythms from heart rate time series such as would be provided by non-invasive devices that do not use a conventional EKG signal. Since a common and high-profile example of an abnormal cardiac rhythm is atrial fibrillation, its detection from heart rate time series alone is an object of the present invention.
Atrial fibrillation is an increasingly common disorder of cardiac rhythm in which the atria depolarize at exceedingly fast rates. Even with normal function of the atrioventricular (AV) node, which serves as the sole electrical connection between the atria and the ventricles and filters the high frequency of atrial impulses, atrial fibrillation can result in heart rates as high as 160 to 180 beats per minute. While these fast rates, along with the lack of atrial contractile function, may or may not cause symptoms, atrial fibrillation carries with it the risk of stroke because the lack of concerted atrial contraction allows blood clots to form. Thus the major emphases in treatment are conversion to normal sinus rhythm (NSR), control of heart rates, and anticoagulation to reduce the risk of stroke.
Patients with severe heart disease are at increased risk of ventricular tachycardia (VT) or fibrillation, and implantable cardioverter-defibrillator (ICD) devices are recommended to reduce the incidence of sudden cardiac death. ICDs are small battery-powered electrical impulse generators which are implanted in at-risk patients and are programmed to detect cardiac arrhythmia and correct it by delivering a jolt of electricity to the heart muscle. These patients are also at high risk of atrial fibrillation leading to inappropriate ICD shocks. While dual chamber devices allow better AF detection because the atrial electrical activity is known, single lead ICDs must rely entirely on the RR interval time series. There is a need to improve detection of AF in short records to reduce inappropriate ICD shocks.
The current management paradigm for patients with atrial fibrillation emphasizes anticoagulation and both heart rate control and attempts to convert to normal sinus rhythm (NSR). This is based on the findings of randomized clinical trials that showed no morbidity or mortality advantage to either rhythm control or rate control strategies as long as anticoagulation was maintained. Some principles that dominate the current practice are: anticoagulation for life once even a single paroxysm of AF has been detected in patients at risk for stroke; higher doses of AV nodal-blocking drugs to lower average heart rates, and more frequent AV junction ablation coupled with permanent electronic cardiac pacing; and cardioversion, anti-arrhythmic drugs and, if they fail, left atrial catheter ablation procedures to restore and maintain sinus rhythm.
Decisions about these therapeutic options are best made if there is accurate estimation of the proportion of time spent in AF, or the “AF burden.” Many patients with AF are elderly, and in some there is a substantial risk of anticoagulation because of the propensity to fall. If indeed an episode of AF were truly never to recur, then the risk of anticoagulation after a few months could legitimately be avoided. There is a need, therefore, for a continuous monitoring strategy to determine the need for continued anticoagulation in patients thought to be free of AF.
Many patients with AF are unaware of persistently fast ventricular rates that would lead the physician to alter medications or to consider AV junction ablation therapy in conjunction with electronic ventricular pacing. This also demonstrates a need for a continuous monitoring strategy that reports descriptive statistics of the heart rate during AF.
Moreover, patients for whom rhythm control is attempted require continuous monitoring to determine the success of the therapy, and the need for further therapies if AF recurs.
Detection of AF can be accomplished with very high degrees of accuracy, if an intra-atrial cardiac electrogram from an implanted pacing lead or a conventional EKG signal from skin electrodes are available. Neither is as non-obtrusive as a device that records the time from one arterial pulse waveform to the next, but such a non-invasive device can provide only the heart rate time series with no information about cardiac electrical activity. Thus an algorithm and computer method for detecting AF in a heart rate or pulse rate series is a desirable goal.
Tateno and Glass developed a measure based on the reasoning that distinctive differences between AF and sinus (or other) rhythms lay in the degree of overall variability. [See: K. Tateno and L. Glass, “Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ΔRR intervals,” Med Biol Eng Comput vol. 39, 664-671, 2001.]
Tateno and Glass used the canonical MIT-BIH Holter databases (www.physionet.org) to develop empirical cumulative distribution functions (ECDFs) of heartbeat interval and heart rates, and to test the hypothesis that a new data set belongs to the AF group. The resulting algorithm, which was based on 90% of the data sets, had diagnostic performance in the remaining 10%, with Receiver Operating Characteristic (ROC) area of 0.98, and sensitivity and specificity over 95% at some cut-offs.
Generally, there are some potential barriers to widespread implementation of the Tateno and Glass approach:
First, the data were collected from a non-random sample of 23 AF patients in the early 1980's when medical practices were different with regard to heart rate-controlling (HR-controlling) drugs and anti-congestive heart failure drugs. The average heart rate in the MIT-BIH Atrial Fibrillation Database is 96 beats per minute, compared to 81 beats per minute in the contemporary University of Virginia Holter Database.
Second, much rests on histograms of intervals occurring within 30-minute blocks that are arbitrarily segregated by the mean heart rate during the 30 minutes. This approach is vulnerable to large changes in results based on small differences in heart rates. Moreover, some ECDFs represent many more patients and data points than others. Choosing histogram boundaries so that each represents the same proportion of the entire database has appeal, but still suffers from inescapable problems when such bright cut-offs are employed.
Third, episodes of AF lasting less than 30 minutes might be missed altogether, if surrounded by very regular rhythms.
Fourth, the MIT-BIH arrhythmia database that was used for testing is relatively small.
Fifth, in the Tateno and Glass approach there is no analysis of the dynamics (i.e., the order) of RR intervals. This is an especially important distinction between the Tateno and Glass approach and the present invention.
With respect to the detection of atrial fibrillation from interbeat intervals, the Tateno and Glass method employs a Kolmogorov-Smirnov (KS) test of ECDFs of observed sets of ΔRR intervals (the difference between one RR interval and the next) versus empirical histograms of ΔRR intervals during atrial fibrillation (AF) obtained from MIT-BIH Atrial Fibrillation Database. The fundamental measurement is the largest distance, also called the KS distance, between ECDFs of observed data and a template data set. Large distances suggest that the data sets represent different cardiac rhythms. The KS distance method of Tateno and Glass is designed to distinguish AF from normal sinus rhythm (NSR) and from other arrhythmias such as paced rhythm, ventricular bigeminy and trigeminy, and others. Formally, the parameter calculated is the probability that the observed intervals arise from AF, thus small p-values (PV) provide evidence that data is not AF. Tateno and Glass suggest a cutoff of PV>0.01 as a diagnostic criterion for AF.
The 16 ECDFs of ΔRR intervals during AF are based on 10,062 non-overlapping 50 point AF episodes segregated by ranges of the mean RR interval distributed as shown in Table 1.
TABLE 1Mean RRSegments350-39938400-449325450-499548500-5491179550-5992114600-6491954650-6991256700-749913750-799386800-849342850-899256900-949331950-9992651000-10491241050-1099241100-11497
There are appealing features of this method. There is nonparametric characterization of ΔRR densities; the mean RR interval is incorporated into the analysis; and it distinguishes AF from normal sinus rhythm (NSR) and from other arrhythmias in the MIT-BIH arrhythmia database.
However, the current art presents further limitations, disadvantages, and problems, in addition to the general limitations noted above.
First, the mean RR interval is not included as continuous variable, but rather in ranges. It is an object of the present invention to address the need for a new method, which utilizes the mean RR interval as a continuous variable.
Second, the empirical cumulative distribution function (ECDF) analysis is not dependent at all on non-AF rhythms. It is an object of the present invention to address the need for a new method wherein the ECDF analysis is dependent on non-AF rhythms.
Third, the analysis requires a large amount of histogram data (>500,000 data points) for implementation. It is an object of the present invention to address the need for a new method, which requires significantly less histogram data.
Fourth, the histograms for low (<400) and high (>1049) mean RR intervals are based on very few segments. It is an object of the present invention to address the need for a new method, which avoids this limitation.
Fifth, there are no histograms for extremely high (>1150) mean RR intervals. It is an object of the present invention to address the need for a new method, which avoids this limitation.
Sixth, the data are not independent, invalidating the theoretical p-value calculation. It is an object of the present invention to address the need for a new method, which utilizes independent data.
The long-felt need for a new method that addresses the limitations, disadvantages, and problems, discussed above, is evidenced by the many databases available for development and testing of new AF detection algorithms. Several databases have been used during the development and testing of the present invention.
The MIT-BIH Atrial Fibrillation (AF) Database, which consists of 10-hour recordings from 23 patients with AF. Each beat has been manually annotated as to its rhythm. In all, there are 299 segments of AF lasting a total of 91.59 hours (40%) and 510,293 beats. The database can be divided into 21,734 non-overlapping 50-point records with following distribution: AF 8320, NSR 12171, other 735 and mixed 508. For modeling of binary outcomes, the database can be considered as 8824 50-point records with any AF and 12,910 with no AF.
The MIT-BIH Arrhythmia (ARH) Database consists of two parts (100 series and 200 series) with 30-minute recordings (1500 to 3400 beats). The 100 series contains 23 subjects (48244 total beats) with no AF, but some other abnormal rhythms (7394 beats). The 200 series contains 25 subjects (64394 total beats) with 8 subjects with AF (12402 beats, 11%); other abnormal rhythms also present (13091 beats). The database can be divided into 2227 non-overlapping 50-point records with 289 (13%) having any AF. The overall distribution was AF 187, NSR 1351, other 255, and mixed 434. The development of new methods to detect atrial fibrillation has been limited, because the current go/no-go decision for developing new AF detection algorithms rests on analysis of the ARH database, which contains only about 2 hours of AF in 8 patients from more than 20 years ago.
Results obtained in the MIT-BIH databases may not hold up in widespread use because of their small sizes and highly selective nature. Accordingly, a more real-world data set of complete RR interval time series from consecutive 24-hour Holter monitor recordings has been analyzed.
The University of Virginia Holter Database consists of 426 consecutive 24-hour recordings from the Heart Station beginning in October, 2005. 206 are from males, and the median age is 58 years (10th percentile 23 years, 90th percentile 80 years). 76 (18%) gave “atrial fibrillation”, “atrial fibrillation/flutter”, or “afib-palpitations” as the reason for the test.
The dynamics of cardiac rhythms can be quantified by entropy and entropy rate under the framework of continuous random variables and stochastic processes [See C. E. Shannon, “A Mathematical Theory of Communication”, Bell System Technical Journal, vol. 27, pp. 379-423 & 623-656, July & October, 1948].
Approximate entropy (ApEn) was introduced in 1991 as a measure that could be applied to both correlated random and noisy deterministic processes with motivation drawn from the fields of nonlinear dynamics and chaos theory [See: S. Pincus, “Approximate entropy as a measure of system complexity,” Proc. Natl. Acad. Sci., vol. 88, pp. 2297-2301, 1991.]. There are limitations and possible pitfalls in the implementation and interpretation of ApEn, especially with the need to detect cardiac rhythms in relatively short data records.
Sample entropy (SampEn) is an alternative measure with better statistical properties and has successfully been utilized on neonatal heart rate data (HR data) to aid in the prediction of sepsis [See J. Richman and J. Moorman, “Physiological time series analysis using approximate entropy and sample entropy,” Amer J Physiol, vol. 278, pp. H2039-H2049, 2000; and D. Lake, J. Richman, M. Griffin, and J. Moorman, “Sample entropy analysis of neonatal heart rate variability,” Amer J Physiol, vol. 283, pp. R789-R797, 2002.]. See also U.S. Pat. No. 6,804,551 to Griffin et al. issued Oct. 12, 2004 and assigned to the same assignee herein. The '551 patent is incorporated herein by reference in its entirety.
SampEn has also been used as part of the promising new multiscale entropy (MSE) analysis technique to better discriminate adult HR data among normal, atrial fibrillation, and congestive heart failure patients [See: M. Costa, A. Goldberger, and C. Peng, “Multiscale entropy analysis of complex physiologic time series,” Phys. Rev. Lett., vol. 89, no. 6, p. 068102, 2002.]. For purposes of comparison, this work is termed the deterministic approach to measuring complexity and order in heart rate variability.
Standard error estimates aid in evaluating the adequacy of the selected matching tolerance r which can be especially crucial for short records. An expression for approximating the variance of sample entropy was presented in D. Lake, J. Richman, M. Griffin, and J. Moorman, “Sample entropy analysis of neonatal heart rate variability,” Amer J Physiol, vol. 283, pp. R789-R797, 2002, and used in selecting optimal values of m and r. Exploiting the special U-statistic structure of SampEn, this estimate has recently been improved upon and asymptotic normality established [See J. Richman, “Sample entropy statistics,” Ph.D. dissertation, University of Alabama Birmingham, 2004]. Estimating the standard error for Apen and other Renyi entropy rate estimates has proved to be more complicated because of the dependency of the random process and the nonlinearities in the calculations.
With deterministic approaches the values of m and r are fixed for all the analysis (sometimes signal length is also constant). This is done to enable comparison of a wider variety of processes, but has several disadvantages. The choices of m and r vary from study to study and comparison of results is not always possible. Methods to optimally choose these parameters have been studied and this process has been a part of developing entropy measures for detecting atrial fibrillation [See: D. Lake, J. Richman, M. Griffin, and J. Moorman, “Sample entropy analysis of neonatal heart rate variability,” Amer J Physiol, vol. 283, pp. R789-R797, 2002.].
Current implanted devices employ a “stability” algorithm based on the variability amongst a small number of interbeat or RR intervals, and “unstable” rhythms are interpreted as AF. The reasoning is that the most distinctive difference between AF and other rhythms lies in the degree of variability.